A range sensor is known as one of sensors which are often installed in mobile robots. The range sensor can detect the distance to objects in the environment using laser beam. However, the measurement ...area of the range sensor is limited because the scanning plane of the sensor is single and parallel to the mounting surface. In this research, we developed a new range sensor capable of scanning in two directions. Using the periodic refraction of laser beam generated by the rotation of two acrylic prisms installed around the range sensor, two-directional scanning is realized. If this range sensor is placed at the front of the mobile robot horizontally, the robot becomes possible to scan both distant area and road surface. In addition, we devised a simple method for the classification and integration of two-directional measurement data based on a model fitting method. In this paper we describe the concept of the range sensor device and explain the method for the classification and integration of two-directional measurement data. Furthermore, we verified the range sensor's effectiveness through object detection experiments.
This paper describes a self-localization for indoor mobile robots based on integrating measurement values from multiple optical mouse sensors and a global camera. This paper consists of two parts. ...Firstly, we propose a dead-reckoning based on increments of the robot movements read directly from the floor using optical mouse sensors. Since the measurement values from multiple optical mouse sensors are compared to each other and only the reliable values are selected, accurate dead-reckoning can be realized compared with the conventional method based on increments of wheel rotations. Secondly, in order to realize robust localization, we propose a method of estimating position and orientation by integrating measured robot position (orientation information is not included) via global camera and dead-reckoning with the Kalman Filter.
This paper describes a self-localization for indoor mobile robots based on integrating measurement values from multiple optical mouse sensors and a global camera. This paper consists of two parts. ...Firstly, we propose a dead-reckoning based on increments of the robot movements read directly from the floor using optical mouse sensors. Since the measurement values from multiple optical mouse sensors are compared to each other and only the reliable values are selected, accurate dead-reckoning can be realized compared with the conventional method based on increments of wheel rotations. Secondly, in order to realize robust localization, we propose a method of estimating position and orientation by integrating measured robot position (orientation information is not included) via global camera and dead-reckoning with the Kalman Filter.
In this paper, we propose a method of self-localization for mobile robots. The method is based upon model-fittng of floor region provided by the omni-directional camera mounted on the robot. In our ...method, even if a part of the floor region is hidden by obstacles, this can be compensated for by computing the convex hull of a boundary point set of the floor region in the omni-directional image. The geometric features of the detected floor region and the linearized least square method considering the properties of omni-directional imaging are employed to fit the known floor shape. In addition, we developed a self-localization method that allowed for better estimates of values by integrating omni-directional vision and dead reckoning with the Kalman filter than by individual methods. Finally, we verify the effectiveness of these methods through several experiments with a real robot according to the rule of the RoboCup Small Size League.
In this paper, in order to realize an accurate localization for mobile robots, we propose a dead-reckoning system based on increments of the robot movements read directly from the floor using optical ...mouse sensors. The movements of two axes are measurable with an optical mouse sensor. Therefore, in order to calculate a robot's deviation of position and orientation, it is necessary to attach two optical mouse sensors in the robot. However, it is also assumed that a sensor cannot read the movements correctly due to the condition of the floor, the shaking of the robot, etc. To solve this problem, we arrange multiple optical mouse sensors around the robot and compare sensor values. By selecting reliable sensor values, accurate dead-reckoning is realized. Finally, we verify the effectiveness of this algorithm through several experiments with an actual robot.
PDF
